基于DeepLabV3+改进的光伏板语义分割模型研究
作者:
作者单位:

太原科技大学电子信息工程学院 太原 030024

中图分类号:

TN29

基金项目:

山西省科技成果转化引导专项(202204021301059)、山西省重点研发计划项目(202202150401005)、山西省重大科技专项计划(202201090301013)、山西省重点研发计划项目(202202010101005)资助


Research on the improved semantic segmentation model of photovoltaic panels based on DeepLabV3+
Author:
Affiliation:

School of Electronic Information Engineering, Taiyuan University of Science and Technology,Taiyuan 030024, China

  • 摘要
  • | |
  • 访问统计
  • | |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    从光伏板的红外图像中分割提取出光伏板区域信息,可以极大提高光伏板故障检测的精度。而传统语义分割算法对光伏板的边界信息处理效果不佳,存在光伏板边界呈波浪状、互相黏连以及背景误分割等情况。针对此类情况,本文提出了一种基于改进DeepLabV3+的光伏板语义分割算法模型,将主干网络更改为MobileNetV2,引入Canny边缘检测算法输出新的浅层特征语义信息;设计SE-ASPP模块对特征通道进行重新校准,增强网络表达能力;增加浅层特征语义信息通道数,加强对浅层特征语义信息的关注。实验结果表明,改进后DeepLabV3+算法模型的精准率、mIoU、召回率和F1分数分别达到99.50%、99.21%、99.61%和99.55%,与原DeepLabV3+模型相比,分别提高了2.24%、1.58%、1.57%和1.72%,在实际分割任务中表现出色,具有更高的检测精度和可靠性。

    Abstract:

    The segmentation and extraction of PV panel region information from infrared images of PV panels can greatly improve the accuracy of PV panel fault detection. However, the traditional semantic segmentation algorithm is not effective in processing the boundary information of PV panels, and there are cases that the boundary of PV panels is wave-like, sticking to each other, and the background is mis-segmented. To solve this situation, this paper proposes a semantic segmentation algorithm model for PV panels based on improved DeepLabV3+, which changes the backbone network to MobileNetV2, introduces the Canny edge detection algorithm to output new shallow feature semantic information, and designs the SE-ASPP module to re-calibrate the feature channels to enhance the network expression capability, and increase the number of channels of shallow feature semantic information to strengthen the attention to shallow feature semantic information. Experimental results show that the precision, mIoU, recall and F1 score of the improved DeepLabV3+ algorithm model reach 99.50%、99.21%、99.61% and 99.55%, respectively, which are 2.24%、1.58%、1.57% and 1.72% higher than the original DeepLabV3+ model, respectively. Improved DeepLabV3+ model performs well in real segmentation tasks and has higher detection accuracy and reliability.

    参考文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

王银,孙海顺,谢刚,赵志诚,谢新林.基于DeepLabV3+改进的光伏板语义分割模型研究[J].电子测量技术,2024,47(22):136-143

复制
分享
文章指标
  • 点击次数:66
  • 下载次数: 87
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 在线发布日期: 2025-01-16
文章二维码